ArticleAmerican journal of cancer research2025
Nomogram integrating clinical-radiological and radiomics features for differentiating invasive from non-invasive pulmonary adenocarcinomas presenting as ground-glass nodules.
Article in American journal of cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Development and validation of a CT-based nomogram for predicting invasiveness in solitary pulmonary ground-glass nodules.American journal of cancer research · 2026Article
- A CT-based multi-scale fusion model with SHAP interpretation for preoperative differentiation between lung adenocarcinoma in situ/minimally invasive adenocarcinoma and invasive adenocarcinoma: a multicenter study.Frontiers in oncology · 2026Article
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11 authors.
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Abstract
objectiveTo construct a nomogram incorporating clinical-radiological and radiomics features from computed tomography (CT) for distinguishing invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) in ground-glass nodules (GGNs).
methodsThis retrospective study included 473 GGN patients with postoperative pathological confirmation of AIS, MIA, or IAC. The training set comprised 257 patients from Yantaishan Hospital, while the test set, used for external validation, included 216 patients from the Affiliated Hospital of Binzhou Medical College. Radiomics features were selected, and a radiomics model was constructed using least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) methods. A clinical-radiological model was developed using univariate and multivariate logistic regression. The nomogram was generated by combining the two models. Its performance was evaluated via receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).
resultsThe radiomics model included 11 features, while the clinical-radiological model incorporated lobulation, age, and long diameter. The nomogram outperformed both individual models in terms of accuracy and area under the curve (AUC) in both the training and test sets. Calibration curve analysis confirmed good consistency between actual and predicted outcomes, and DCA indicated the nomogram's clinical utility.
conclusionThe nomogram is a non-invasive, accurate tool for preoperative differentiation of GGN types, providing valuable guidance for clinicians in treatment planning.
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